More generally, the two examples and our definitions highlight gap in ML specific OPE literature suggesting OPE literature has largely focused on i) interventional analysis through off-policy evaluation, ii) focusing on estimates of the population, such as expectations, as opposed to individuals or rarely sub-populations and iii) assuming identifiability from observational data, thereby making tacit assumptions about the underlying data-generating procedure [1]}, [2]}, [3]}.
Applications like medical domains easily motivate the need for generalization along the lines summarized in Figure REF  as we are usually interested in evaluating patient level utility of policies, not just prospectively, but also retrospectively. In the following we discuss implications for OPE based on its formalization as a causal estimand. In particular, we propose that these distinctions be routinely highlighted, thereby clearly exposing the underlying assumptions and hence applicability of corresponding estimands [1]}, [3]}, [2]}, [7]}. We demonstrate the main utility of this exposition and the proposed generalizations specifically along these factors of OPE has practical implications. More specifically, these implications become clear by tying the associated estimation challenges to specific sources of uncertainties in the system.
